Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add autohandai/community-skills --skill code-reviewgit clone --depth 1 https://github.com/autohandai/community-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/autohandai/community-skills/code-review)<a href="https://agentmods.dev/skills/autohandai/community-skills/code-review"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/code-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/autohandai/community-skills/code-review"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.02173 |
| Opus 5.5 | $0.00020 | $0.00869 |
| Sonnet 5.5 | $0.00010 | $0.00435 |
| Haiku 4.5 | $0.00005 | $0.00217 |
Grade A, and why
code-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
95% identical to code-review — 9 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 393 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
When to use this skill
- Reviewing pull requests
- Checking code quality
- Providing feedback on implementations
- Identifying potential bugs
- Suggesting improvements
- Security audits
- Performance analysis
Instructions
Step 1: Understand the context
Read the PR description:
- What is the goal of this change?
- Which issues does it address?
- Are there any special considerations?
Check the scope:
- How many files changed?
- What type of changes? (feature, bugfix, refactor)
- Are tests included?
Step 2: High-level review
Architecture and design:
- Does the approach make sense?
- Is it consistent with existing patterns?
- Are there simpler alternatives?
- Is the code in the right place?
Code organization:
- Clear separation of concerns?
- Appropriate abstraction levels?
- Logical file/folder structure?
Step 3: Detailed code review
Naming:
- Variables: descriptive, meaningful names
- Functions: verb-based, clear purpose
- Classes: noun-based, single responsibility
- Constants: UPPER_CASE for true constants
- Avoid abbreviations unless widely known
Functions:
- Single responsibility
- Reasonable length (< 50 lines ideally)
- Clear inputs and outputs
- Minimal side effects
- Proper error handling
Classes and objects:
- Single responsibility principle
- Open/closed principle
- Liskov substitution principle
- Interface segregation
- Dependency inversion
Error handling:
- All errors caught and handled
- Meaningful error messages
- Proper logging
- No silent failures
- User-friendly errors for UI
Code quality:
- No code duplication (DRY)
- No dead code
- No commented-out code
- No magic numbers
- Consistent formatting
Step 4: Security review
Input validation:
- All user inputs validated
- Type checking
- Range checking
- Format validation
Authentication & Authorization:
- Proper authentication checks
- Authorization for sensitive operations
- Session management
- Password handling (hashing, salting)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 393 lines · 49 tokens per session scan A 95b1d9c73416
code-review is a skill published in the GitHub repository autohandai/community-skills (11 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 49 tokens to every session and 2,173 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to code-review, differing in 9 lines, and is treated as a copy.
Other skills, from other repositories
agentic-review
Use when reviewing current branch for bugs before pushing or merging, when wanting a thorough multi-agent review of local changes, or when preparing work for human review. Not for codebase structure, not for code style, and not for fixing what it finds.
simplify-code
Sequential 3-lens cleanup of recent code changes.
codespaces
Build and query architecture-aware code search, dependency graphs, call flows, impact analysis, and belief maps for Python, TypeScript, TSX, Rust, C#, Java, Go, Ruby, and Ruby on Rails repositories. Use before non-trivial code changes to find module boundaries, blast radius, architecture violations, and the minimal…
ai-slop-cleaner
Use when a working code path feels bloated, noisy, or over-abstracted, when asked to clean up or deslop AI-generated code, or when a reviewer-only anti-slop pass is requested.
hqe
Comprehensive codebase health auditing, remediation, and verification skill based on the canonical HQE Protocol v5.0.0.
code-change-impact
Trace the impact (blast radius) of a code change in ANY codebase and prove it didn't break anything else — language- and framework-agnostic. Use RIGHT AFTER a fix to a feature, function, module, type, route, API endpoint, schema, or config, before committing, opening a PR, or pushing. Triggers: "did this break…